Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

Abstract

Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.

Does Conventional Morphological Evaluation Still Play a Role in Predicting Blastocyst Formation?

“…This method, however, requires a culture duration up to 96 hours, which may resulted in a delayed decision making. A recent study integrates deep learning algorithms to the time-lapse system, and the predictive power in terms of AUC reaches 0.82 [8]. In comparison the historical performance of time-lapse system in predicting blastocyst formation, the conventional static observation of the old era yields acceptable predictive power and only requires limited resource.…”

“…In recent years,'OMICS' technologies [6], and algorithms created through the use of time-lapse microscopy [7] were used to predict the destiny of day 3 embryos during in vitro culture. While 'OMICS' technologies, such as proteomics and metabolomics for non-invasive embryo developmental capacity assessment, are yet to be recommended for routine use [1], time-lapse microscopy has been introduced as a routine clinical practice and showed a capacity to predict the blastocyst formation with AUCs ranging from 0.6-0.8 across different studies [8][9][10][11][12][13][14][15][16][17][18][19]. Unfortunately, novel technologies inevitably require additional cost or equipment and the expense of technologies may limit their widespread use.…”

Artificial intelligence in the embryology laboratory: a review

“…Interestingly, more weights were given to the morphokinetic data compared to the images. When compared to embryologists, the AI model performed better in terms of sensitivity and specificity (Liao et al, 2021). In another TLM study, BL prediction was accomplished by using morphokinetic TLM data from the first three days of development.…”

NMN treatment reverses unique deep radiomic signature morphology of oocytes from aged mice

“…This recent surge in interest has led to several attempts to apply AI methodologies to the assessment of embryo viability for human assisted reproduction, although success has been variable [6,7]. AI methods have been proposed to automate sperm, embryo assessment through morphology analysis such as time laps imaging [8][9][10][11][12].…”